There are many different ML models. More complex models may perform better than others, when given large datasets or a large number of input features. Simpler models may be faster for small-scale tasks. For any ML model, its hyperparameters play an important role. These can pertain to the architecture of the ML model or can be directly connected to its training. The number of hidden layers and nodes for an ANN is an example of the first category, while hyperparameters such as the learning algorithm, the amount of the learning rate or the type of kernel functions for the SVMs are part of the second category. The values of the hyperparameters can be set via several heuristic techniques, such as grid search, random search and others [1]}.
